Anomaly Detection for Asynchronous Multivariate Time Series of Nuclear Power Plants Using a Temporal-Spatial Transformer.

Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.

Study on a terahertz biosensor based on graphene-metamaterial.

The interaction between the terahertz wave propagating in free space and the sample is weak, which leads to the weak signal of the sample, which cannot meet the detection needs of trace samples. In order to meet the detection of trace samples, a kind of metamaterial absorber (the basic unit of the absorber is composed of gold-high resistance silicon-aluminum three-layer structure) is designed, and the monolayer graphene is transferred on the surface of the metamaterial absorber to construct the graphene-metamaterial absorber heterostructure. The transmission spectrum of the resonant cavity is simulated and measured by terahertz time domain spectroscopy system, and the obvious resonance frequency shift is observed. The results show that the graphene-metamaterial absorber heterostructure can detect josamycin antibiotic solution with concentration of 0.02 mg/L (the mass of josamycin is 0.2 ng). Compared with using the same structure metamaterial absorber to detect josamycin antibiotics, the sensitivity is increased by an order of magnitude. Using graphene-metamaterial heterostructure to detect the relative change of heterostructure reflectivity caused by josamycin antibiotics can reach 40%. The research in this paper provides a new technical means for accurate and rapid detection in terahertz band.

Distributed multi-camera multi-target association for real-time tracking.

Tracking and associating different views of the same target across moving cameras is challenging as its appearance, pose and scale may vary greatly. Moreover, with multiple targets a management module is needed for new targets entering and old targets exiting the field of view of each camera. To address these challenges, we propose DMMA, a Distributed Multi-camera Multi-target Association for real-time tracking that employs a target management module coupled with a local data-structure containing the information on the targets. The target management module shares appearance and label information for each known target for inter-camera association. DMMA is designed as a distributed target association that allows a camera to join at any time, does not require cross-camera calibration, and can deal with target appearance and disappearance. The various parts of DMMA are validated using benchmark datasets and evaluation criteria. Moreover, we introduce a new mobile-camera dataset comprising six different scenes with moving cameras and objects, where DMMA achieves 92% MCTA on average. Experimental results show that the proposed tracker achieves a good association accuracy and speed trade-off by working at 32 frames per second (fps) with high definition (HD) videos.

Cluster-Delay Mean Square Consensus of Stochastic Multi-Agent Systems with Impulse Time Windows.

This paper investigates the cluster-delay mean square consensus problem of a class of first-order nonlinear stochastic multi-agent systems with impulse time windows. Specifically, on the one hand, we have applied a discrete control mechanism (i.e., impulsive control) into the system instead of a continuous one, which has the advantages of low control cost, high convergence speed; on the other hand, we considered the existence of impulse time windows when modeling the system, that is, a single impulse appears randomly within a time window rather than an ideal fixed position. In addition, this paper also considers the influence of stochastic disturbances caused by fluctuations in the external environment. Then, based on algebraic graph theory and Lyapunov stability theory, some sufficiency conditions that the system must meet to reach the consensus state are given. Finally, we designed a simulation example to verify the feasibility of the obtained results.